| GEC: A Unified Framework for Interactive Decision Making in MDP, POMDP, and Beyond H Zhong, W Xiong, S Zheng, L Wang, Z Wang, Z Yang, T Zhang arXiv preprint arXiv:2211.01962, 2022 | 50* | 2022 |
| Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation X Chen, H Zhong, Z Yang, Z Wang, L Wang International Conference on Machine Learning, 3773-3793, 2022 | 44 | 2022 |
| Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game W Xiong, H Zhong, C Shi, C Shen, L Wang, T Zhang arXiv preprint arXiv:2205.15512, 2022 | 42 | 2022 |
| Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-constraint W Xiong, H Dong, C Ye, Z Wang, H Zhong, H Ji, N Jiang, T Zhang ICLR 2024 Workshop on Mathematical and Empirical Understanding of Foundation …, 2023 | 40* | 2023 |
| Can Reinforcement Learning Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopically Rational Followers? H Zhong, Z Yang, Z Wang, MI Jordan Journal of Machine Learning Research 24 (35), 1-52, 2023 | 40* | 2023 |
| Pessimistic minimax value iteration: Provably efficient equilibrium learning from offline datasets H Zhong, W Xiong, J Tan, L Wang, T Zhang, Z Wang, Z Yang International Conference on Machine Learning, 27117-27142, 2022 | 38 | 2022 |
| Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration Z Liu, M Lu, W Xiong, H Zhong, H Hu, S Zhang, S Zheng, Z Yang, Z Wang Thirty-seventh Conference on Neural Information Processing Systems, 2023 | 25* | 2023 |
| A Self-Play Posterior Sampling Algorithm for Zero-Sum Markov Games W Xiong, H Zhong, C Shi, C Shen, T Zhang International Conference on Machine Learning, 24496-24523, 2022 | 25 | 2022 |
| Why robust generalization in deep learning is difficult: Perspective of expressive power B Li, J Jin, H Zhong, J Hopcroft, L Wang Advances in Neural Information Processing Systems 35, 4370-4384, 2022 | 21 | 2022 |
| A theoretical analysis of optimistic proximal policy optimization in linear markov decision processes H Zhong, T Zhang Advances in Neural Information Processing Systems 36, 2024 | 19 | 2024 |
| Double pessimism is provably efficient for distributionally robust offline reinforcement learning: Generic algorithm and robust partial coverage J Blanchet, M Lu, T Zhang, H Zhong Advances in Neural Information Processing Systems 36, 2024 | 18 | 2024 |
| Optimistic Policy Optimization is Provably Efficient in Non-stationary MDPs H Zhong, Z Yang, Z Wang, C Szepesvári arXiv preprint arXiv:2110.08984, 2021 | 18 | 2021 |
| Nearly optimal policy optimization with stable at any time guarantee T Wu, Y Yang, H Zhong, L Wang, S Du, J Jiao International Conference on Machine Learning, 24243-24265, 2022 | 13 | 2022 |
| DPO Meets PPO: Reinforced Token Optimization for RLHF H Zhong, G Feng, W Xiong, L Zhao, D He, J Bian, L Wang arXiv preprint arXiv:2404.18922, 2024 | 9 | 2024 |
| Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference Adjustment R Yang, X Pan, F Luo, S Qiu, H Zhong, D Yu, J Chen arXiv preprint arXiv:2402.10207, 2024 | 8 | 2024 |
| Towards Robust Offline Reinforcement Learning under Diverse Data Corruption R Yang, H Zhong, J Xu, A Zhang, C Zhang, L Han, T Zhang arXiv preprint arXiv:2310.12955, 2023 | 7 | 2023 |
| Tackling heavy-tailed rewards in reinforcement learning with function approximation: Minimax optimal and instance-dependent regret bounds J Huang, H Zhong, L Wang, L Yang Advances in Neural Information Processing Systems 36, 2024 | 6 | 2024 |
| Provable Sim-to-real Transfer in Continuous Domain with Partial Observations J Hu, H Zhong, C Jin, L Wang arXiv preprint arXiv:2210.15598, 2022 | 6 | 2022 |
| Breaking the Moments Condition Barrier: No-Regret Algorithm for Bandits with Super Heavy-Tailed Payoffs H Zhong, J Huang, L Yang, L Wang Advances in Neural Information Processing Systems 34, 2021 | 6 | 2021 |
| A Reduction-Based Framework for Conservative Bandits and Reinforcement Learning Y Yang, T Wu, H Zhong, E Garcelon, M Pirotta, A Lazaric, L Wang, SS Du International Conference on Learning Representations, 2021/9/29, 2021 | 6* | 2021 |
Liwei WangProfessor, Peking UniversityVerified email at cis.pku.edu.cn
